Face tracking is usually a process of determining the face's movement and change of size in a video or an image sequence. Face tracking plays an important role and is widely used in image analysis and recognition, image monitoring and retrieval, instant video communication, etc.
Generally, the face tracking processing can mainly include identifying a face location in a video. During video recording, when a face moves, a specific algorithm such as a particle shift or a mean shift can be used to track the specific location of the face in the video. Currently, face tracking method processing used in the existing technology mainly include: performing face detection on each image frame, that is, each frame can be considered as a separate image, and then face detection is performed on each image frame, so as to obtain a face location in each image frame by calculation. However, in actual implementations, for example, in a process in which a user uses a front-facing camera of a mobile phone to take a selfie, a face tracking loss or a detection error is usually caused due to a sudden light or scene change, strong light or metering interference, rapid face movement, etc. Consequently, tracked face images are usually discontinuous in a user video monitoring process or a video call process, and a real-time smooth tracking effect cannot be achieved, thereby greatly compromising user experience, especially in a terminal device with relatively poor processing performance. Certainly, the face tracking method in the existing technology cannot meet a relatively high face-tracking requirement of a user.
For the face tracking methods in the existing technology, a face tracking loss or an error occurs especially in a complex scenario such as a sudden light change, light interference, or rapid face movement, which can result in a blurred face image or discontinuous tracked face images in a video. The effectiveness of face detection and tracking, and user experience may be compromised.